The prognosis predictive score around primary debulking surgery (PPSP) improves diagnostic efficacy in predicting the prognosis of ovarian cancer

In recent years, the pretreatment inflammatory responses have proven to predict the prognosis, but no report exists analyzing the combined inflammatory response of the pre- and postsurgical treatment. The current study aims to extract the factors predicting the recurrence and create novel predictive scoring. This retrospective study was conducted at our institution between November 2006 and December 2020, with follow-up until September 2022. Demographic and clinicopathological data were collected from women who underwent primary debulking surgery. We created the scoring system named the prognosis predictive score around primary debulking surgery(PPSP) for progression-free survival(PFS). Univariate and multivariate analyses were performed to assess its efficacy in predicting PFS and overall survival(OS). Cox regression analyses were used to assess its time-dependent efficacy. Kaplan–Meier and the log-rank test were used to compare the survival rate. A total of 235 patients were included in the current study. The cut-off value of the scoring system was six. Multivariate analyses revealed that an advanced International Federation of Gynecology and Obstetrics(FIGO) stage (p < 0.001 for PFS; p = 0.038 for OS), the decreased white blood cell count difference (p = 0.026 for PFS) and the high-PPSP (p = 0.004 for PFS; p = 0.002 for OS) were the independent prognostic factors. Cox regression analysis also supported the above results. The PPSP showed good prognostic efficacy not only in predicting the PFS but also OS of ovarian cancer patients comparable to FIGO staging.

www.nature.com/scientificreports/ index (SII) 37,38 have been shown to have good prognostic value. In this context, it is suggested that the tumor microenvironment has an extraordinary effect on the systemic immune system, and reduced inflammatory status after surgery should be a strong impact on the prognosis. However, no predictive scoring system exists based on pre-and post-PDS predictive factors. Actually, patients underwent surgery have to wait nervously for the effect of the adjuvant chemotherapy and the physician has to follow up strictly with all patients. This study aims to seek the prognostic factors related to recurrence around PDS in ovarian cancer, create the prognostic score predicting the prognosis of post-PDS ovarian cancer, and analyze the usefulness of the scoring.

Results
From November 2006 and December 2020, a total of 235 patients were included in this study. Patient's peripheral blood data were collected at the first hospitalization before and after PDS, and the median days from PDS were 25 days. A total of 183(77.8%) patients underwent chemotherapy after surgery. Among the patients who did not underwent chemotherapy, 45(86.5%) patients were the stage I. The recurrence and non-recurrence cases were 68(28.9%) and 167(71.1%) cases, respectively. The demographic and clinical characteristics of the current cohort are outlined in Table 1. The recurrence cases showed trends in older age and advanced stages. Seroustype tumor tended to have higher recurrence rate than other tumor subtypes. In the current cohort, there was no significant differentiation in the distribution of peripheral blood cells before PDS (Table2). The carbohydrate antigen125(CA125), C-reactive protein(CRP), and the D-dimer reached significant differentiation between the non-recurrent and recurrent patients. The results of the ROC curve analysis bases on the detection of recurrence are shown in Table 3. The optimal cutoff value was determined by analyzing the ROC curve predicting the recurrence. The ROC analysis showed the same result as peripheral blood markers before treatment, white blood cell counts, CRP, and albumin after PDS showed an efficacy. Moreover, the difference in white blood cell counts showed efficacy (Table 3, Fig. 1A). Table 4 shows the distribution of the above candidates related to preand post-PDS assessment. PPSP is defined by older age (≥ 55 years), elevated pretreatment CA125 (≥ 124.5 U/ mL), pretreatment CRP (≥ 0.26 mg/dL), and pretreatment D-dimer (≥ 1.1 µg/mL), and post-PDS white blood cell count (≥ 57.00 × 10 2 /µL), post-PDS CRP (≥ 0.08 mg/dL), post-PDS hypo-albuminemia (< 4.0 g/dL), and white blood cell counts difference ([post-PDS counts -pre-pretreatment counts] ≥ -29.00 × 10 2 /µL), if all parameters are www.nature.com/scientificreports/ abnormal, the assigned value is 8; and if all parameters are normal, the assigned value is 0. We next assessed the efficacy of the PPSP in discriminating between non-recurrent and recurrent cases. The result of the ROC curve analysis based on the discriminating non-recurrent and recurrent cases is shown in Fig. 1B  www.nature.com/scientificreports/ factors. Log lank analysis revealed that low-PPSP (< 6) showed good prognostic efficacy in both PFS and OS (p < 0.001)( Fig. 2A,B). Even divided into early or advanced stages according to FIGO staging as I/II or III/IV, PPSP showed good efficacy to predict PFS and OS other than PFS in stage III/IV (Fig. 2C-F).

Discussion
Several studies have been reported to predict PFS and OS in ovarian cancer using pre-treatment factors, at least to our knowledge there is no prognostic scoring system consisting both of pre-and post-PDS patients' data. The current study revealed that the PPSP showed great efficacy in predicting PFS and OS, which were comparable to FIGO staging. The CA125 was considered the most promising serum marker of ovarian cancer 39 . It has been thought that higher preoperative serum CA125 levels are directly related to a larger tumor burden 40,41 , and there have been numerous discussions about whether the CA125 level could predict optimal surgical cytoreduction 42 . In this context, CA125 reflects not only the tumor burden but also the carcinomatosis [43][44][45] . In the current study, the Table 4. Post-PDS peripheral blood cell and serum markers. CRP C-reactive protein, PDS primary debulking surgery.  www.nature.com/scientificreports/ pre-treatment CA125 was extracted in the scoring system regardless of tumor subtype, which could reflect the peritoneal inflammation rather than tumor burden, partly because the current study did not include only CA125 productive tumors. CRP is synthesized by hepatocytes. It is a non-specific yet sensitive marker of acute inflammatory response and is expressed in selected neoplastic cells 46 . Numerous studies have indicated that an increased CRP level value indicates poor prognosis in various types of cancer [47][48][49][50] . Albumin, similarly, is generally used for assessing nutritional status 46 . Malnutrition and inflammation suppress albumin synthesis, thereby reducing immune defense, impeding treatment response, and contributing to adverse outcomes in patients with cancer 51 . Malignant tumors also consume such nutrition as albumin 52 , leading to edema and cachexia, which have been reported to be correlated with an unfavorable prognosis for some gastrointestinal tumors 53,54 . Moreover, the GPS, a cumulative inflammation-based cancer-prognostic marker composed of serum elevation of CRP and decrease in albumin concentration, is likely to reflect host systemic inflammatory response and has been reported to be significant as a prognostic indicator in cancer-bearing patients [55][56][57] . In the current study, these CRP and albumin were also extracted as a candidate for prognosis poor outcomes in ovarian cancer patients, comparable to these reports. d-dimer, a soluble fibrin-degradation product, is a valuable marker for diagnosing venous thromboembolism 58 . The d-dimer test is frequently positive for venous thromboembolism and inflammatory autoimmune disease as rheumatoid arthritis, cancer, elderly age, surgery, trauma, pregnancy, and postpartum. We previously reported that a high pre-treatment plasma d-dimer level was one of the independent risk factors of overall survival 59 . d-dimer could be another significant inflammatory factor that predicts the outcome of ovarian cancer. Numerous reports, on ovarian cancer, have created evidence that NLR, LMR, and PLR including platelet count may be helpful indicators for differentiating benign neoplasms from malignant changes 60,61 . Moreover, they are sensitive indicators correlated with local advancement and response to first-line chemotherapy. However, we did not find the effectiveness of the true platelet, neutrophil, monocyte, and lymphocyte counts. Instead, we found the prognostic evidence of post-PDS white blood cell counts and their difference. This scoring system shared rather the factors with GPS/mGPS 34,36 and leukocytosis 25,26 than NLR, LMR, and PLR 60 . This method could be more useful for the physician.
This study has some limitations. The first limitation is that we did not compare the PPSP with such predictive scoring as NLR, LMR, PLR, GPS/mGPS, and SII as a nature of new reporting of the novel scoring system. Second, we did not investigate the cases of interval debulking surgery cases mainly administrated in firstly inoperative cases because the peripheral blood counts were dramatically altered by the chemotherapy. We will report a novel scoring system around interval debulking surgery in the near future.
In conclusion, The PPSP showed good prognostic efficacy not only in predicting the PFS but also OS of ovarian cancer patients comparable to FIGO staging. Statistical analysis. Analyses were performed using SPSS version 25.0 (IBM SPSS, Armonk, NY, USA). The differences of each factor were compared using a Mann-Whitney U test. The receiver operating characteristic(ROC) curve analysis was performed to determine the cut-off value for predicting poor prognosis. www.nature.com/scientificreports/ The cut-off value was based on the highest Youden index (i.e., sensitivity + specificity − 1). We used a logistic regression analysis to assess the risk factors for poor prognosis. And to assess its time dependent prognosis efficacy cox regression analyses and log rank test were selected. A two-sided p < 0.05 was considered as indicating a statistically significant difference.

Data availability
The datasets generated during and analyzed during the current study are available from the corresponding author on reasonable request.